A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures
- We itemize the security aspects of SF and PA and establish a map between attacks and security aspects.
- We use CKC  for the first time to present a systematic taxonomy on cyber-threats to SF and PA.
- We study the anatomy as well as the behavioral characteristics of APT.
- Finally, we develop a future road map to highlight some related emerging areas that still need to be studied in future research.
2. Existing Reviews
3. Cyber-Attacks on SF and PA
- Integrity: guarantees information not to be changed during storage or transmission. Integrity of PA and SF has been part of the topic in several research projects .
- Availability guarantees the continuity of the provided services. Some recent research works have focused on the availability of SF and PA .
- Non-Repudiation keeps users from repudiating what they have done in the system. The importance of non-repudiation in SF and PA has received attention form a few researchers .
- Trust: makes it impossible for a user to spoof another identity. The literature in this area comes with a few research projects focusing on authenticity of SF and PA .
- Attacks on Hardware: Unknown or unprotected vulnerabilities of IoT and cyber-physical devices (as well as other hardware components) may be exploited by professional attackers using specialised tools . As good examples for this kind of attacks, we may refer to side channel and Radio frequency (RF) jamming attacks, which can violate privacy, confidentiality, or authenticity when they hit poorly-designed IoT and cyber-physical systems [2,66,67,68].
- Attacks on the Network and Related Equipment: target the network or the connected devices. This category of attacks can be further categorized as follows.
- Denial of Service (DoS) [70,71] prevents users or devices from their authorized access to a resource such as a node, a server, or a communication link/network. For example, Radio Frequency (RF) Jamming [2,72] overwhelms the RF spectrum used by a network aiming to deny communication services to the connected nodes.
- Botnets [2,73,74] are groups of Internet-connected devices (remote sensors), each running one bot or more. Botnets can be used for many different purposes, including Distributed DoS (DDoS) attacks, information stealing, SPAM dissemination. They can be designed to violate availability, integrity, or trust.
- Cloud Computing Attacks  misuse cloud features such as self-provisioning, on-demand services and auto-scaling to take advantage of cloud resources. For instance, an infected virtual machine can quickly spread the infecting malware to other virtual machines via the cloud. These attacks may target non-repudiation, trust, or integrity.
- Attacks on Data: hit the data while being stored, transmitted, or processed in the system. This category of attacks can be divided into the following subcategories .
- Misconfiguration  is the action of configuring the SF or the PA reporting systems in a way that reflects invalid information regarding the managed farm, which can lead to costly, disruptive decisions and actions from the farmers. Misconfiguration attacks violate the integrity.
- Attacks on Code (applications):
- Software Update Attacks  violate the integrity and the availability of the system via disrupting the update process of the installed software.
- Attacks on Support Chain: are designed to hit different components of the support chain.
- Data Fabrication  involves the creation of malicious data or processes misusing an access provided for another purpose. It can lead to the violation of the system’s integrity.
- Misuse Attacks: include attacks that misuse SF and PA physical resources in order to conduct attacks on other entities such as people or institutes.
- Cyber-Terrorism  may use IoT systems and cyber-physical devices to attack people or premises from afar. This can lead to the violation of trust in SF and PA systems.
4. CKC-Based Taxonomy on Security Threats to SF and PA
- Reconnaissance, where the attacker starts to identify and profile the victim via gathering as much information as possible. Any relevant information, such as email addresses, can be of interest in the reconnaissance stage. The primary goal of this stage is to discover the vulnerabilities of the victim. If properly accomplished, this stage can facilitate and accelerate a cyber-attack and make it difficult to detect via identifying the weak and strong points of the victim system. Moreover, the reconnaissance itself should not be suspicious to security mechanisms in the victim’s system. We can consider a passive and active approach for Reconnaissance. In the context of a cyberattack, passive Reconnaissance is known as footprinting. Active Reconnaissance is commonly referred to as scanning. An easy scan would be to ping every IP address owned by the destination network to see which ones went to real hosts. More sophisticated exploitation methods connect to every port number of the IP address to determine what services run on that host and which ports are open. In contrast to footprinting, scanning provides more specific information but is more intrusive. Additionally, the target may be alerted to a potential attack since scanning can trigger more abnormal connections, which must be avoided when scanning.
- Weaponization, wherein the attacker designs and implements the remote access malware (the weapon) e.g., the backdoor, virus or worm tailored to the vulnerabilities of the victim (discovered in the reconnaissance phase).
- Delivery, in which the attacker launches the remote access malware onto the victim (e.g., via a USB device, an e-mail attachment or a website).
- Exploitation, which triggers the remote access malware. In this stage, the attacker utilizes the remote access malware to action on the victim and the related network in order to exploit vulnerability.
- Installation, where the attacker tries to get permanent access to the victim via installing proper Command and Control (C2) servers.
- C2, wherein the attacker communicates with the C2 server in order to control the victim.
- Action on Target, where the attacker completes the attack scenario and achieves the final goal by compromising the victim.
4.2. The Taxonomy
4.2.1. Threats Related to the Reconnaissance Stage
- Threats to the multi-layer architecture: These threats target different layers of the multi-layered architecture of Figure 1 as explained below.
- Edge Layer: SF and PA environments rely on the edge layer devices for their real-time or near real-time computations and services. In some scenarios, attackers may be able to shutdown the function of these devices, causing disruption, delay, customer dissatisfaction, and financial loss .
4.2.2. Threats Related to the Weaponization Stage
- IoT Device Level Evasion: It has been observed that defence in IoT device level mainly relies on anti-viruses and end-point security solutions embedded in or installed on devices, which detect well-known virus signatures or anomalies in behaviors. Well-designed attacks on SF and PA try to hide themselves from these mechanisms. As an example, one may refer to hollowing technique or heap spraying, which may successfully embed a malicious code inside an application even in the case the anti-virus includes the related signature [90,91,92,93].
- Network Level Evasion: Firewalls and Intrusion Detection/Prevention Systems (IDSs/IPSs) are the most common tools for in-network protection [94,95]. A key point to be reiterated here is that there is no completely secure solution, including firewalls and IDS/IPS. Although these tools can detect a malicious executable file, they may be unable to detect a malicious file attached to an email. Thus, they are not considered as completely-secure mechanisms in SF and PA. To evade these mechanisms, SF and PA attacks depend on a wide range of techniques, among which we can mention illegal use of well-known protocols (HTTPS, DNS, HTTP, etc.)  or ports (53, 80, 443, etc.)  as well as network spoofing .
4.2.3. Threats Related to the Delivery Stage
- Indirect Penetration: In the indirection penetration scenario, a communication protocol, a gateway or a web application may play the role of the trusted third party. Trusted third parties can help attackers gather information via TCP/UDP port scan, spoofing and sniffing, or launch a pre-designed backdoor [1,3].
4.2.4. Threats Related to the Exploitation Stage
- Exploiting Software and OS Vulnerabilities: Zero-day exploits are good examples for this kind of threat . They are not detectable by common software vulnerability protection mechanisms. They exploit unknown software vulnerabilities for which no patch or fix is available. Moreover, viruses, worms, Trojan horses, and backdoors are common threats related to OS vulnerabilities in smart devices and consequently in SF and PA [104,105].
- SQL injection: In recent years, SQL injection has frequently hit data-driven applications using code injection techniques . It can potentially hit SF and PA databases as well.
4.2.5. Threats Related to the Installation Stage
- Modifying registry keys 
- DLL Search Order Hijacking 
- DLL side loading 
- Startup Folder Modification 
4.2.6. Threats Related to the C2 Stage
- C2s using Network Protocols: Many common attacks on SF and PA use HTTP/HTTPS, ICMP, DNS, FTP, SMTP and other standard network protocols for their communications in the C2 stage [119,120]. For example, in many scenarios, when direct connection to an external mail server or an agriculture database server is not possible, hackers rely on backdoors that use protocols such as FTP or SMTP to penetrate into the server via sending files or emails [37,101]. Moreover, to bypass common network security mechanisms, hackers may perturb DNS packets, which makes the attack more difficult to trace .
- C2s using Removable Media: Given the features of removable media (such as USB storage), they are commonly used to bypass networks for the exfiltration of data. For example, when a disk is formatted for decreasing the size of a partition, hundreds of megabytes of data (including malicious files) can be stored at the last addresses of the disk without being lost .
4.2.7. Threats Related to the Action Stage
4.2.8. Threats Related to More Than One Stage
4.3. Case Study
4.3.1. The Target Environment
4.3.2. Anatomy of the Attack
Action on Target:
5. APTs in SF and PA
5.1. An Introduction to APT Attacks
5.2. APT Attacks on SF and PA
- Theft of IP (patents, etc.)
- Stealth of critical data related to food chain, control, genetics, etc.
- Damage to important agricultural infrastructure (change to database entries or control parameters)
The Anatomy of an APT Attack on SF or PA
- Penetration (Infiltration):This step can be mapped to the first three stages in CKC. Social engineering can be mentioned as a common reconnaissance technique used in this step . This phase may involve vulnerability exploiting and malicious code uploading (SQL Injection). Penetration may lead to the installation of backdoors, which can provide the attacker with further access to the network.This step is composed of the following phases.
- Testing the target for detection
- White noise attack
- Initial infiltration
- Outbound connection initiation
- Further Access (Expansion):This step may be mapped stages 4 through 6 in CKC. In this step, the attacker tries to gain longer access to the network or access to more strategic resources. The goal is getting control of critical functions and manipulating them to pave the way for the final step. This step is composed of the following phases. The following two functions can be run in this step.
- Expanding access and stealing credentials using phishing and similar techniques
- Broadening the presence
- Information Stealth and Sabotage (Exploitation):We can compare the final step to the last stage in CKC. In this step, APT groups may steal valuable information, shut down a strategic function, or cause damage to the system.Critical business information regarding the production value or cultivation process of livestock or crops may be of interest to APT groups in this step. These kinds of information can be sold to a rival or used to undermine the production process. The stolen data may be stored somewhere inside the victim network for covert transportation in the future.APT attacks commonly use white noise techniques (maybe in the form of a DDoS attack) to evade detection systems .This step is typically composed of the following phases.
- White noise attack
- Extra data collection
- Covering tracks
5.3. Some Behavioral Characteristics of APT Attacks on SF or PA
- Unusual and suspicious activities in security and control systems (especially the high-level access systems)
- Widespread use of backdoor tools (Most of them can be detected by common IDSs.
- Suspicious and unusual activities occurring in the databases
- Evidences for data and information stealth
6. Risk Mitigation Strategies and Countermeasure
|Strategy||IoT Layer||Related Security Aspect|
|User Authentication [136,137,138,139,140,141,142,143]||Middleware||Privacy,|
|Secure Passwords [137,138,139,140,141,142,143,144]||Middleware||Privacy,|
|Device-Level Encryption [136,137,138,139,140,143,144]||Edge layer||Privacy,|
|Remote Login Deactivation [137,138,139,140,141,142,143]||Internet,|
|Regular Firmware Update [3,138,139,140,141,142,144,145]||Application||Privacy,|
|Measure||IoT Layer||Related Security Aspect|
Made Safe [65,137,138,139,140,141,144]
|Unauthorized Account Deactivation [138,139,140,142,144]||Edge layer||Privacy,|
|Periodic Device Evaluation [65,137,139,140,141,143,144]||Edge layer||Privacy,|
7. Future Roadmap
- Access Control from a Security Perspective:Dealing with hired labor and livestock, the owners of farms, greenhouses, etc. are traditionally concerned about access control. However, they need to adopt a security perspective for their property. Authentication, authorization, and accounting should be incorporated to prevent unauthorized access, which is the stepping stone in many severe attacks on SF and PA. Although the literature in this area comes with some relevant research reports [1,2,3,41,50], there is still opportunities for systematic research.
- Data Protection:Given, the abilities of smart devices and IoT, enormous data acquisition, communication and processing is a well-known characteristic of SF and PA [1,2]. This raises the need for efficient data protection mechanisms, which has been of interest to a few researchers [1,2]. However, data protection can still be considered as an open research challenge in this area.
- Network Infrastructure and Physical Layer Protection:
- Education as Risk Reduction: The social elements of reducing cyber-risks, such as education, has received relatively little attention by scholars . Not only are farmers are interested in education regarding security threats to their operations, but education is regarded as a vital tool for reducing security risk [132,147,148]. Future research opportunities exist in assessing what cyber-safe behaviours farmers currently utilize. From this, there can be investigation of what educational formats, content, and dispersion would be most effective and appreciated by these vital stakeholders.
- Specific Secure Communication Protocols for SF and PA:In SF and PA, where sensors, actuators, drones, autonomous tractors and other smart devices are spread out in various locations, communication protocols are of critical importance. Although several network, Machine-to-Machine (M2M) and IoT protocols such as Bluetooth, NFC, WiFi, SSL, TCP, UDP and 6loWPA have been adopted from other technologies [3,121,149], there is still room for research on secure protocols specifically designed and standardized for SF and PA.
- Secure Smart Devices: Given the threats to smart devices used in SF and PA (studied in previous sections), security needs to be taken into consideration in design and implementation phases of these devices.
- Secure AI Adoption: In recent years, some researchers have been concerned about the application of AI in SF and PA [2,3]. Thus, the security threats to AI such as adversarial attacks [1,2] can motivate future research on secure AI in SF and PA. Informed by this taxonomy of cyber-threats, strengthening policy regarding cyber-security in PA is an important consideration for future research. Legal frameworks designed to protect private data and ensure privacy are key to addressing the challenge of cyber-security arising from new farming technologies . Reviews on the social science of PA have highlighted that future research needs to address how policy-makers should respond to cyber-attacks on agri-food systems .
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Reviews||Objective & Contribution||Considering Technology|
|||Study on security and privacy in SF ecosystems|
Elaborates on potential cyber-attack scenarios
Open research challenges and future directions
|IoT, Wireless Sensor Network (WSN),|
Blockchain, AI, Machine Learning (ML)
|||Study on security threats in SF and agricultural IoT|
Highlights the innovations, techniques, and threats in SF
|IoT, Blockchain, AI, ML|
|||Applying ML methods for PA|
Demonstrates the impact of ML in improving the quality of the product
|IoT, AI, ML|
|||Studies the data mining approaches for the management of PA|
Applying Fuzzy, DBSCAN, SVM, algorithms
|||Using AI algorithms for formulating yield prediction in PA|
Finding the best classification algorithm, bagging, in crops
|||Designing energy-saving sensors for PA|
Measures temperature, soil moisture, and humidity by sensors in SF
|||Study WSN methods in PA and SF|
Plant monitoring with the image processing using field-programmable gate array (FPGA)
|||Presents the importance of PA over traditional agriculture techniques|
Applying WSN for obtaining parameters of land
|||Reviews WSN applications in SF and PA||IoT, WSN|
|||Design WSNs and smart humidity sensors for PA|
Comparative review of research in the field of agriculture
Very large-scale integration (VLSI)
|||The review outlines the applications of WSNs in agriculture|
Provides a taxonomy of energy-efficient techniques for WSNs in agriculture
Shows opportunities for processing IoT data
|||Considers Raspberry Pi as visual sensor nodes in PA|
Applying random forest and support vector machine classifiers to classify crops
|WSN, AI, ML|
|||Studies ranging and imaging techniques for PA|
Develops sensing techniques to provide information about crop growth
Presents innovative sensing methods in pesticide management and crop monitoring
|||Studies Routing Protocols for WSN in PA|
Applies Ad-Hoc, MANET, VANET and WSN to facilitate control of PA
|MANET, VANET, WSN|
|||Studies energy efficient routing protocol for IoT Based PA||IoT, WSN|
|||Review on SF with Zinc-Fortified sprouts|
Considers robotic solutions with AI in agricultural techniques
|||Review on role of IoT in SF|
Discusses different tools, hardware, and software used in SF
|||Studies the IoT effect when implementing SF|
Highlights security issues in IoT in agriculture
Presents open research issues and challenges in IoT agriculture
|IoT, WSN, AI, ML|
|||Review on IoT-based Multidisciplinary models for SF|
Considers Cyber-Physical systems role in PA
Applies cloud computing technologies for better production of crops
|IoT, WSN, Cloud computing|
|||Explores the advantages of using deep learning in SF|
Provides a bibliography containing 120 papers in SF and PA
|IoT, AI, ML|
|||Studies cyber-security challenges in SF|
using an empirical methodology to highlight security threats in SF systems
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Yazdinejad, A.; Zolfaghari, B.; Azmoodeh, A.; Dehghantanha, A.; Karimipour, H.; Fraser, E.; Green, A.G.; Russell, C.; Duncan, E. A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures. Appl. Sci. 2021, 11, 7518. https://doi.org/10.3390/app11167518
Yazdinejad A, Zolfaghari B, Azmoodeh A, Dehghantanha A, Karimipour H, Fraser E, Green AG, Russell C, Duncan E. A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures. Applied Sciences. 2021; 11(16):7518. https://doi.org/10.3390/app11167518Chicago/Turabian Style
Yazdinejad, Abbas, Behrouz Zolfaghari, Amin Azmoodeh, Ali Dehghantanha, Hadis Karimipour, Evan Fraser, Arthur G. Green, Conor Russell, and Emily Duncan. 2021. "A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures" Applied Sciences 11, no. 16: 7518. https://doi.org/10.3390/app11167518